GANs for Anti Money Laundering; Running AI Models in the Cloud

Berlin Machine Learning Group
Berlin Machine Learning Group
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Talk 1: GANs for Anti Money Laundering

Speaker: Jim Dowling

Abstract: Deep learning delivers a lower number of false positives with higher accuracy than traditional rule-based approaches to anti money laundering (AML). However, supervised machine learning is not a viable approach due to the massive imbalance between the number of “good” and “bad” transactions. In this talk, we will go unsupervised and present our work on using GANs for anomaly detection for AML.

Bio: Jim Dowling is CEO of Logical Clocks and an Associate Professor at KTH Royal Institute of Technology. He is lead architect of the open-source Hopsworks platform, a horizontally scalable data platform for machine learning that includes the industry’s first Feature Store.

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Talk 2: Running AI Models in the Cloud

Speaker: Maurits Kaptein

Abstract: In healthcare and beyond many useful ML and AI models have been developed over the last decade. However, only a fraction of these models actually makes it into healthcare practice. During this talk Maurits will discuss why this is the case and how, using WebAssembly as a ML/AI deployment target, he and his team are trying to make model deployment efficient and flexible.

Bio: Maurits Kaptein is a professor of Data Science and Health at Tilburg University and the Jheronimus Academy of Data Science. His research work focusses on sequential experimentation and causal inference. Maurits is also the co-founder of Scailable (see https://www.scailable.net).